Task 2: Unemployment Analysis!

Importing Libraries

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import plotly.express as px
%matplotlib inline
import seaborn as sns

import warnings
warnings.filterwarnings("ignore")

Importing data

data = pd.read_csv("Unemployment in India.csv");

data

	Region	Date	Frequency	Estimated Unemployment Rate (%)	Estimated Employed	Estimated Labour Participation Rate (%)	Area
0	Andhra Pradesh	31-05-2019	Monthly	3.65	11999139.0	43.24	Rural
1	Andhra Pradesh	30-06-2019	Monthly	3.05	11755881.0	42.05	Rural
2	Andhra Pradesh	31-07-2019	Monthly	3.75	12086707.0	43.50	Rural
3	Andhra Pradesh	31-08-2019	Monthly	3.32	12285693.0	43.97	Rural
4	Andhra Pradesh	30-09-2019	Monthly	5.17	12256762.0	44.68	Rural
...	...	...	...	...	...	...	...
763	NaN	NaN	NaN	NaN	NaN	NaN	NaN
764	NaN	NaN	NaN	NaN	NaN	NaN	NaN
765	NaN	NaN	NaN	NaN	NaN	NaN	NaN
766	NaN	NaN	NaN	NaN	NaN	NaN	NaN
767	NaN	NaN	NaN	NaN	NaN	NaN	NaN

768 rows × 7 columns

data.shape

(768, 7)

Check if their is any Null values present in the data!

data.isnull().sum()

Region                                      28
 Date                                       28
 Frequency                                  28
 Estimated Unemployment Rate (%)            28
 Estimated Employed                         28
 Estimated Labour Participation Rate (%)    28
Area                                        28
dtype: int64

data.dropna(inplace=True)

data.isnull().sum()

Region                                      0
 Date                                       0
 Frequency                                  0
 Estimated Unemployment Rate (%)            0
 Estimated Employed                         0
 Estimated Labour Participation Rate (%)    0
Area                                        0
dtype: int64

We have dropped the null values

data.shape

(740, 7)

data.describe()

	Estimated Unemployment Rate (%)	Estimated Employed	Estimated Labour Participation Rate (%)
count	740.000000	7.400000e+02	740.000000
mean	11.787946	7.204460e+06	42.630122
std	10.721298	8.087988e+06	8.111094
min	0.000000	4.942000e+04	13.330000
25%	4.657500	1.190404e+06	38.062500
50%	8.350000	4.744178e+06	41.160000
75%	15.887500	1.127549e+07	45.505000
max	76.740000	4.577751e+07	72.570000

data.describe().round()

	Estimated Unemployment Rate (%)	Estimated Employed	Estimated Labour Participation Rate (%)
count	740.0	740.0	740.0
mean	12.0	7204460.0	43.0
std	11.0	8087988.0	8.0
min	0.0	49420.0	13.0
25%	5.0	1190404.0	38.0
50%	8.0	4744178.0	41.0
75%	16.0	11275490.0	46.0
max	77.0	45777509.0	73.0

data.corr()

	Estimated Unemployment Rate (%)	Estimated Employed	Estimated Labour Participation Rate (%)
Estimated Unemployment Rate (%)	1.000000	-0.222876	0.002558
Estimated Employed	-0.222876	1.000000	0.011300
Estimated Labour Participation Rate (%)	0.002558	0.011300	1.000000

data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 740 entries, 0 to 753
Data columns (total 7 columns):
 #   Column                                    Non-Null Count  Dtype  
---  ------                                    --------------  -----  
 0   Region                                    740 non-null    object 
 1    Date                                     740 non-null    object 
 2    Frequency                                740 non-null    object 
 3    Estimated Unemployment Rate (%)          740 non-null    float64
 4    Estimated Employed                       740 non-null    float64
 5    Estimated Labour Participation Rate (%)  740 non-null    float64
 6   Area                                      740 non-null    object 
dtypes: float64(3), object(4)
memory usage: 46.2+ KB

We can observe that their are three columns with float datatype and four columns with object datatype!

Data Wrangling

data.sample(10)

	Region	Date	Frequency	Estimated Unemployment Rate (%)	Estimated Employed	Estimated Labour Participation Rate (%)	Area
152	Karnataka	31-10-2019	Monthly	7.13	12803527.0	43.10	Rural
326	Uttar Pradesh	31-01-2020	Monthly	6.06	45777509.0	39.67	Rural
529	Jharkhand	31-03-2020	Monthly	16.40	2480661.0	43.34	Urban
642	Punjab	31-05-2020	Monthly	20.54	2682658.0	34.71	Urban
387	Assam	31-05-2019	Monthly	7.87	1671707.0	44.92	Urban
331	Uttar Pradesh	30-06-2020	Monthly	8.51	41908909.0	36.89	Rural
278	Tamil Nadu	31-07-2019	Monthly	6.01	16455928.0	53.94	Rural
143	Jharkhand	31-03-2020	Monthly	5.01	7157454.0	37.96	Rural
214	Meghalaya	30-04-2020	Monthly	8.38	803118.0	48.83	Rural
546	Karnataka	30-06-2020	Monthly	6.12	8822411.0	39.93	Urban

#Converting the date into datetime format

data['Date']=pd.to_datetime(data[' Date'])

data

	Region	Date	Frequency	Estimated Unemployment Rate (%)	Estimated Employed	Estimated Labour Participation Rate (%)	Area	Date
0	Andhra Pradesh	31-05-2019	Monthly	3.65	11999139.0	43.24	Rural	2019-05-31
1	Andhra Pradesh	30-06-2019	Monthly	3.05	11755881.0	42.05	Rural	2019-06-30
2	Andhra Pradesh	31-07-2019	Monthly	3.75	12086707.0	43.50	Rural	2019-07-31
3	Andhra Pradesh	31-08-2019	Monthly	3.32	12285693.0	43.97	Rural	2019-08-31
4	Andhra Pradesh	30-09-2019	Monthly	5.17	12256762.0	44.68	Rural	2019-09-30
...	...	...	...	...	...	...	...	...
749	West Bengal	29-02-2020	Monthly	7.55	10871168.0	44.09	Urban	2020-02-29
750	West Bengal	31-03-2020	Monthly	6.67	10806105.0	43.34	Urban	2020-03-31
751	West Bengal	30-04-2020	Monthly	15.63	9299466.0	41.20	Urban	2020-04-30
752	West Bengal	31-05-2020	Monthly	15.22	9240903.0	40.67	Urban	2020-05-31
753	West Bengal	30-06-2020	Monthly	9.86	9088931.0	37.57	Urban	2020-06-30

740 rows × 8 columns

Extracting the year and month from the date

year=[i.strftime('%Y') for i in list(data['Date'])]
month=[i.strftime('%B') for i in list(data['Date'])]

data['Year']=year
data['Month']=month

data

	Region	Date	Frequency	Estimated Unemployment Rate (%)	Estimated Employed	Estimated Labour Participation Rate (%)	Area	Date	Year	Month
0	Andhra Pradesh	31-05-2019	Monthly	3.65	11999139.0	43.24	Rural	2019-05-31	2019	May
1	Andhra Pradesh	30-06-2019	Monthly	3.05	11755881.0	42.05	Rural	2019-06-30	2019	June
2	Andhra Pradesh	31-07-2019	Monthly	3.75	12086707.0	43.50	Rural	2019-07-31	2019	July
3	Andhra Pradesh	31-08-2019	Monthly	3.32	12285693.0	43.97	Rural	2019-08-31	2019	August
4	Andhra Pradesh	30-09-2019	Monthly	5.17	12256762.0	44.68	Rural	2019-09-30	2019	September
...	...	...	...	...	...	...	...	...	...	...
749	West Bengal	29-02-2020	Monthly	7.55	10871168.0	44.09	Urban	2020-02-29	2020	February
750	West Bengal	31-03-2020	Monthly	6.67	10806105.0	43.34	Urban	2020-03-31	2020	March
751	West Bengal	30-04-2020	Monthly	15.63	9299466.0	41.20	Urban	2020-04-30	2020	April
752	West Bengal	31-05-2020	Monthly	15.22	9240903.0	40.67	Urban	2020-05-31	2020	May
753	West Bengal	30-06-2020	Monthly	9.86	9088931.0	37.57	Urban	2020-06-30	2020	June

740 rows × 10 columns

EDA

data.columns

Index(['Region', ' Date', ' Frequency', ' Estimated Unemployment Rate (%)',
       ' Estimated Employed', ' Estimated Labour Participation Rate (%)',
       'Area', 'Date', 'Year', 'Month'],
      dtype='object')

plt.figure(figsize=(19,14))
sns.barplot(x=data[" Estimated Unemployment Rate (%)"], y=data[" Estimated Labour Participation Rate (%)"]);

Area wise unemployment analysis

area_unemploy=data.groupby(['Area'],as_index=False)[' Estimated Unemployment Rate (%)'].mean()
area_unemploy_plot=sns.barplot(data=area_unemploy,x='Area',y=' Estimated Unemployment Rate (%)')
for bar in area_unemploy_plot.patches:
    area_unemploy_plot.annotate(format(bar.get_height(), '.2f'),
                   (bar.get_x() + bar.get_width() / 2,
                    bar.get_height()), ha='center', va='center',
                   size=12, xytext=(0, 8),
                   textcoords='offset points')

We can conclude that Urban is having 13.17 of unemployment and Rural is having 10.32 of unemployment

Which state has the most data

count1 = data.Region.value_counts()

plt.figure(figsize=(13,10))
sns.barplot(count1.index, count1.values, alpha=0.8)
plt.ylabel('Number of Occurrences', fontsize=12)
plt.xlabel('Region', fontsize=13)
plt.title('Count the Region', fontsize=16)
plt.xticks(rotation='vertical')
plt.show()

Take the mean of rate state by state

grouped = data.groupby(["Region"])[" Estimated Unemployment Rate (%)"].aggregate("mean").reset_index()

plt.figure(figsize=(13,10))
sns.pointplot(grouped['Region'].values, grouped[' Estimated Unemployment Rate (%)'].values, alpha=0.8)
plt.ylabel('Mean rate', fontsize=13)
plt.xlabel('Region', fontsize=13)
plt.title("Average of mean", fontsize=16)
plt.xticks(rotation='vertical')
plt.show()

unemploment = data[["Region", " Estimated Unemployment Rate (%)"]]
fig = px.sunburst(unemploment, path=["Region"], 
                     values=" Estimated Unemployment Rate (%)", 
                     width=700, height=700, color_continuous_scale="RdY1Gn", 
                     title="Unemployment Rate in India")

fig.show()

Unemployment rate is highest in Uttar Pradesh over 27% while lowest in Meghalayas at around 4% !

area_emp=data.groupby(['Area'],as_index=False)[' Estimated Employed'].mean()
area_emp_plot=sns.barplot(data=area_emp,x='Area',y=' Estimated Employed')
for bar in area_emp_plot.patches:
    area_emp_plot.annotate(format(bar.get_height(), '.2f'),
                   (bar.get_x() + bar.get_width() / 2,
                    bar.get_height()), ha='center', va='center',
                   size=12, xytext=(0, 8),
                   textcoords='offset points')

plot=sns.barplot(x='Year',y=' Estimated Unemployment Rate (%)',data=data.groupby('Year',as_index=False)[' Estimated Unemployment Rate (%)'].mean())

for bar in plot.patches:
 
  plot.annotate(format(bar.get_height(), '.2f'),
                   (bar.get_x() + bar.get_width() / 2,
                    bar.get_height()), ha='center', va='center',
                   size=15, xytext=(0, 8),
                   textcoords='offset points')

As we can see that the rate from 2019 increased drastically from 9.40 to around 15.10 in just a year!

Conclusions:

~~ We can conclude that Urban is having 13.17 of unemployment and Rural is having 10.32 of unemployment ~~ Unemployment rate is highest in Tripura over 27% while lowest in Meghalayas at around 4% ! ~~ As we can see that the rate from 2019 increased drastically from 9.40 to around 15.10 in just a year!